COVID-19 Detection from Pulmonary CT Images using Neural Networks based on Dropout-Driven Hidden Layers

Enhanced diagnosis with considerably good sensitivity and specificity is highly indispensable for COVID-19 diagnosis using radiological data to combat hazardous viral infection. Accuracy of diagnosis is a very important part that helps in further triaging and disease management. Artificial intelligent techniques using Convolutional Neural Networks and their modified alternatives have been recognized to be the salvation in chaotic situations and emergencies. Despite their immense ability to give quality results, they suffer from overfitting problems which have to be reduced by regularizing the networks. Dropout is one such regularization that modifies the network to achieve improved performance by discarding the unwanted nodes in the network layers. A simple neural network architecture inspired by former renowned architectures with dropout-driven hidden layers, CVDNN is built and experimented with for various dropout probabilities (0.1, 0.25, 0.5 and 0.75). The model was also tested with different numbers of dense layers: CVDNN1 with a single dense layer and CVDNN2 with two dense layers of a fixed dropout probability of 0.5 in it. The models are trained and tested with pulmonary computed tomography images to distinguish COVID-19 abnormality against normal cases. The CVDNN2 model presents better functioning with improved performance measures than CVDNN1 with an accuracy of 92.86 % accuracy, 90.21% sensitivity and a specificity of 95.52% for the dataset used. Dropout probabilities of 0.25 and 0.5 present reliable and better results compared to the other values experimented with. Hence a dropout-driven hidden layer can enhance the neural network's performance by choosing either 0.25 or 0.5 preferably for different applications. © 2023 IEEE.

Radiological Analysis of COVID-19 Using Computational Intelligence: A Broad Gauge Study.

Pulmonary medical image analysis using image processing and deep learning approaches has made remarkable achievements in the diagnosis, prognosis, and severity check of lung diseases. The epidemic of COVID-19 brought out by the novel coronavirus has triggered a critical need for artificial intelligence assistance in diagnosing and controlling the disease to reduce its effects on people and global economies. This study aimed at identifying the various COVID-19 medical imaging analysis models proposed by different researchers and featured their merits and demerits. It gives a detailed discussion on the existing COVID-19 detection methodologies (diagnosis, prognosis, and severity/risk detection) and the challenges encountered for the same. It also highlights the various preprocessing and post-processing methods involved to enhance the detection mechanism. This work also tries to bring out the different unexplored research areas that are available for medical image analysis and how the vast research done for COVID-19 can advance the field. Despite deep learning methods presenting high levels of efficiency, some limitations have been briefly described in the study. Hence, this review can help understand the utilization and pros and cons of deep learning in analyzing medical images.